Category Archives: Innovation

I taught a fun three hours on the Industrial Revolution in my innovation PhD course this week. The absolutely incredible change in the condition of mankind that began in a tiny corner of Europe in an otherwise unremarkable 70-or-so years is totally fascinating. Indeed, the Industrial Revolution and its aftermath are so important to human history that I find it strange that we give people PhDs in social science without requiring at least some study of what happened.

My post today draws heavily on Joel Mokyr’s lovely, if lengthy, summary of what we know about the period. You really should read the whole thing, but if you know nothing about the IR, there are really five facts of great importance which you should be aware of.

1) The world was absurdly poor from the dawn of mankind until the late 1800s, everywhere. Somewhere like Chad or Nepal today fares better on essentially any indicator of development than England, the wealthiest place in the world, in the early 1800s. This is hard to believe, I know. Life expectancy was in the 30s in England, infant mortality was about 150 per 1000 live births, literacy was minimal, and median wages were perhaps 3 to 4 times subsistence. Chad today has a life expectancy of 50, infant mortality of 90 per 1000, a literacy of 35%, and urban median wages of roughly 3 to 4 times subsistence. Nepal fares even better on all counts. The air from the “dark, Satanic mills” of William Blake would have made Beijing blush, “night soil” was generally just thrown on to the street, children as young as six regularly worked in mines, and 60 to 80 hours a week was a standard industrial schedule.

The richest places in the world were never more than 5x subsistence before the mid 1800s

Despite all of this, there was incredible voluntary urbanization: those dark, Satanic mills were preferable to the countryside. My own ancestors were among the Irish that fled the Potato famine. Mokyr’s earlier work on the famine, which happened in the British Isles after the Industrial Revolution, suggest 1.1 to 1.5 million people died from a population of about 7 million. This is similar to the lower end of the range for percentage killed during the Cambodian genocide, and similar to the median estimates of the death percentage during the Rwandan genocide. That is, even in the British Isles, famines that would shock the world today were not unheard of. And even if you wanted to leave the countryside, it may have been difficult to do so. After Napoleon, serfdom remained widespread east of the Elbe river in Europe, passes like the “Wanderbucher” were required if one wanted to travel, and coercive labor institutions that tied workers to specific employers were common. This is all to say that the material state of mankind before and during the Industrial Revolution, essentially anywhere in the world, would be seen as outrageous deprivation to us today; palaces like Versailles are not representative, as should be obvious, of how most people lived. Remember also that we are talking about Europe in the early 1800s; estimates of wages in other “rich” societies of the past are even closer to subsistence.

2) The average person did not become richer, nor was overall economic growth particularly spectacular, during the Industrial Revolution; indeed, wages may have fallen between 1760 and 1830.

The standard dating of the Industrial Revolution is 1760 to 1830. You might think: factories! The railroad! The steam engine! High Britannia! How on Earth could people have become poorer? And yet it is true. Brad DeLong has an old post showing Bob Allen’s wage reconstructions: Allen found British wages lower than their 1720 level in 1860! John Stuart Mill, in his 1870 textbook, still is unsure whether all of the great technological achievements of the Industrial Revolution would ever meaningfully improve the state of the mass of mankind. And Mill wasn’t the only one who noticed, there were a couple of German friends, who you may know, writing about the wretched state of the Working Class in Britain in the 1840s as well.

3) Major macro inventions, and growth, of the type seen in England in the late 1700s and early 1800s happened many times in human history.

The Iron Bridge in Shropshire, 1781, proving strength of British iron

The Industrial Revolution must surely be “industrial”, right? The dating of the IR’s beginning to 1760 is at least partially due to the three great inventions of that decade: the Watt engine, Arkwright’s water frame, and the spinning jenny. Two decades later came Cort’s famous puddling process for making strong iron. The industries affected by those inventions, cotton and iron, are the prototypical industries of England’s industrial height.

But if big macro-inventions, and a period of urbanization, are “all” that defines the Industrial Revolution, then there is nothing unique about the British experience. The Song Dynasty in China saw the gun, movable type, a primitive Bessemer process, a modern canal lock system, the steel curved moldboard plow, and a huge increase in arable land following public works projects. Netherlands in the late 16th and early 17th century grew faster, and eventually became richer, than Britain ever did during the Industrial Revolution. We have many other examples of short-lived periods of growth and urbanization: ancient Rome, Muslim Spain, the peak of the Caliphate following Harun ar-Rashid, etc.

We care about England’s growth and invention because of what followed 1830, not what happened between 1760 and 1830. England was able to take their inventions and set on a path to break the Malthusian bounds – I find Galor and Weil’s model the best for understanding what is necessary to move from a Malthusian world of limited long-run growth to a modern world of ever-increasing human capital and economic bounty. Mokyr puts it this way: “Examining British economic history in the period 1760-1830 is a bit like studying the history of Jewish dissenters between 50 B.C. and 50 A.D. At first provincial, localized, even bizarre, it was destined to change the life of every man and women…beyond recognition.”

4) It is hard for us today to understand how revolutionary ideas like “experimentation” or “probability” were.

In his two most famous books, The Gifts of Athena and The Lever of Riches, Mokyr has provided exhausting evidence about the importance of “tinkerers” in Britain. That is, there were probably something on the order of tens of thousands of folks in industry, many not terribly well educated, who avidly followed new scientific breakthroughs, who were aware of the scientific method, who believed in the existence of regularities which could be taken advantage of by man, and who used systematic processes of experimentation to learn what works and what doesn’t (the development of English porter is a great case study). It is impossible to overstate how unusual this was. In Germany and France, science was devoted mainly to the state, or to thought for thought’s sake, rather than to industry. The idea of everyday, uneducated people using scientific methods somewhere like ar-Rashid’s Baghdad is inconceivable. Indeed, as Ian Hacking has shown, it wasn’t just that fundamental concepts like “probabilistic regularities” were difficult to understand: the whole concept of discovering something based on probabilistic output would not have made sense to all but the very most clever person before the Enlightenment.

The existence of tinkerers with access to a scientific mentality was critical because it allowed big inventions or ideas to be refined until they proved useful. England did not just invent the Newcomen engine, put it to work in mines, and then give up. Rather, England developed that Newcomen engine, a boisterous monstrosity, until it could profitably be used to drive trains and ships. In Gifts of Athena, Mokyr writes that fortune may sometimes favor the unprepared mind with a great idea; however, it is the development of that idea which really matters, and to develop macroinventions you need a small but not tiny cohort of clever, mechanically gifted, curious citizens. Some have given credit to a political system, or to the patent system, for the widespread tinkering, but the qualitative historical evidence I am aware of appears to lean toward cultural explanations most strongly. One great piece of evidence is that contemporaries wrote often about the pattern where Frenchmen invented something of scientific importance, yet the idea diffused and was refined in Britain. Any explanation of British uniqueness must depend on Britain’s ability to refine inventions.

5) The best explanations for “why England? why in the late 1700s? why did growth continue?” do not involve colonialism, slavery, or famous inventions.

First, we should dispose of colonialism and slavery. Exports to India were not particularly important compared to exports to non-colonial regions, slavery was a tiny portion of British GDP and savings, and many other countries were equally well-disposed to profit from slavery and colonialism as of the mid-1700s, yet the IR was limited to England. Expanding beyond Europe, Dierdre McCloskey notes that “thrifty self-discipline and violent expropriation have been too common in human history to explain a revolution utterly unprecedented in scale and unique to Europe around 1800.” As for famous inventions, we have already noted how common bursts of cleverness were in the historic record, and there is nothing to suggest that England was particularly unique in its macroinventions.

To my mind, this leaves two big, competing explanations: Mokyr’s argument that tinkerers and a scientific mentality allowed Britain to adapt and diffuse its big inventions rapidly enough to push the country over the Malthusian hump and into a period of declining population growth after 1870, and Bob Allen’s argument that British wages were historically unique. Essentially, Allen argues that British wages were high compared to its capital costs from the Black Death forward. This means that labor-saving inventions were worthwhile to adopt in Britain even when they weren’t worthwhile in other countries (e.g., his computations on the spinning jenny). If it worthwhile to adopt certain inventions, then inventors will be able to sell something, hence it is worthwhile to invent certain inventions. Once adopted, Britain refined these inventions as they crawled down the learning curve, and eventually it became worthwhile for other countries to adopt the tools of the Industrial Revolution. There is a great deal of debate about who has the upper hand, or indeed whether the two views are even in conflict. I do, however, buy the argument, made by Mokyr and others, that it is not at all obvious that inventors in the 1700s were targeting their inventions toward labor saving tasks (although at the margin we know there was some directed technical change in the 1860s), nor it is even clear that invention overall during the IR was labor saving (total working hours increased, for instance).

Mokyr’s Editor’s Introduction to “The New Economic History and the Industrial Revolution” (no RePEc IDEAS page). He has a followup in the Journal of Economic History, 2005, examining further the role of an Enlightenment mentality in allowing for the rapid refinement and adoption of inventions in 18th century Britain, and hence the eventual exit from the Malthusian trap.

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How do the social returns to R&D differ from the private returns? We must believe there is a positive gap between the two given the widespread policies of subsidizing R&D investment. The problem is measuring the gap: theory gives us a number of reasons why firms may do more R&D than the social optimum. Most intuitively, a lot of R&D contains “business stealing” effects, where some of the profit you earn from your new computer chip comes from taking sales away from me, even if you chip is only slightly better than mine. Business stealing must be weighed against the fact that some of the benefits of knowledge a firm creates is captured by other firms working on similar problems, and the fact that consumers get surplus from new inventions as well.

My read of the literature is that we don’t have know much about how aggregate social returns to research differ from private returns. The very best work is at the industry level, such as Trajtenberg’s fantastic paper on CAT scans, where he formally writes down a discrete choice demand system for new innovations in that product and compares R&D costs to social benefits. The problem with industry-level studies is that, almost by definition, they are studying the social return to R&D in ex-post successful new industries. At an aggregate level, you might think, well, just include the industry stock of R&D in a standard firm production regression. This will control for within-industry spillovers, and we can make some assumption about the steepness of the demand curve to translate private returns given spillovers into returns inclusive of consumer surplus.

There are two problems with that method. First, what is an “industry” anyway? Bloom et al point out in the present paper that even though Apple and Intel do very similar research, as measured by the technology classes they patent in, they don’t actually compete in the product market. This means that we want to include “within-similar-technology-space stock of knowledge” in the firm production function regression, not “within-product-space stock of knowledge”. Second, and more seriously, if we care about social returns, we want to subtract out from the private return to R&D any increase in firm revenue that just comes from business stealing with slightly-improved versions of existing products.

Bloom et al do both in a very interesting way. First, they write down a model where firms get spillovers from research in similar technology classes, then compete with product market rivals; technology space and product market space are correlated but not perfectly so, as in the Apple/Intel example. They estimate spillovers in technology space using measures of closeness in terms of patent classes, and measure closeness in product space based on the SIC industries that firms jointly compete in. The model overidentifies the existence of spillovers: if technological spillovers exist, then you can find evidence conditional on the model in terms of firm market value, firm R&D totals, firm productivity and firm patent activity. No big surprises, given your intuition: technological spillovers to other firms can be seen in every estimated equation, and business stealing R&D, though small in magnitude, is a real phenomenon.

The really important estimate, though, is the level of aggregate social returns compared to private returns. The calculation is non-obvious, and shuttled to an online appendix, but essentially we want to know how increasing R&D by one dollar increases total output (the marginal social return) and how increasing R&D by one dollar increases firm revenue (marginal private return). The former may exceed the latter if the benefits of R&D spill over to other firms, but the latter may exceed the former is lots of R&D just leads to business stealing. Note that any benefits in terms of consumer surplus are omitted. Bloom et al find aggregate marginal private returns on the order of 20%, and social returns on the order of 60% (a gap referred to as “29.2%” instead of “39.2%” in the paper; come on, referees, this is a pretty important thing to not notice!). If it wasn’t for business stealing, the gap between social and private returns would be ten percentage points higher. I confess a little bit of skepticism here; do we really believe that for the average R&D performing firm, the marginal private return on R&D is 20%? Nonetheless, the estimate that social returns exceed private returns is important. Even more important is the insight that the gap between social and private returns depends on the size of the technology spillover. In Bloom et al’s data, large firms tend to do work in technology spaces with more spillovers, while small firms tend to work on fairly idiosyncratic R&D; to greatly simplify what is going on, large firms are doing more general R&D than the very product-specific R&D small firms do. This means that the gap between private and social return is larger for large firms, and hence the justification for subsidizing R&D might be highest for very large firms. Government policy in the U.S. used to implicitly recognize this intuition, shuttling R&D funds to the likes of Bell Labs.

All in all an important contribution, though this is by no means the last word on spillovers; I would love to see a paper asking why firms don’t do more R&D given the large private returns we see here (and in many other papers, for that matter). I am also curious how R&D spillovers compare to spillovers from other types of investments. For instance, an investment increasing demand for product X also increases demand for any complementary products, leads to increased revenue that is partially captured by suppliers with some degree of market power, etc. Is R&D really that special compared to other forms of investment? Not clear to me, especially if we are restricting to more applied, or more process-oriented, R&D. At the very least, I don’t know of any good evidence one way or the other.

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(One quick PSA before I get to today’s paper: if you happen, by chance, to be a graduate student in the social sciences in Toronto, you are more than welcome to attend my PhD seminar in innovation and entrepreneurship at the Rotman school which begins on Wednesday, the 7th. I’ve put together a really wild reading list, so hopefully we’ll get some very productive discussions out of the course. The only prerequisite is that you know some basic game theory, and my number one goal is forcing the economists to read sociology, the sociologists to write formal theory, and the whole lot to understand how many modern topics in innovation have historical antecedents. Think of it as a high-variance cross-disciplinary educational lottery ticket! If interested, email me at kevin.bryanATrotman.utoronto.ca for more details.)

Back to Aghion et al. Let’s kick off 2015 with one of the nicer pieces to come out the ridiculously productive decade or so of theoretical work on growth put together by Philippe Aghion and his coauthors; I wish I could capture the famous alacrity of Aghion’s live presentation of his work, but I fear that’s impossible to do in writing! This paper is based around writing a useful theory to speak to two of the oldest questions in the economics of innovation: is more competition in product markets good or bad for R&D, and is there something strange about giving a firm IP (literally a grant of market power meant to spur innovation via excess rents) at the same time as we enforce antitrust (generally a restriction on market power meant to reduce excess rents)?

Aghion et al come to a few very surprising conclusions. First, the Schumpeterian idea that firms with market power do more R&D is misleading because it ignores the “escape the competition” effect whereby firms have high incentive to innovate when there is a large market that can be captured by doing so. Second, maximizing that “escape the competition” motive may involve making it not too easy to catch up to market technological leaders (by IP or other means). These two theoretical results imply that antitrust (making sure there are a lot of firms competing in a given market, spurring new innovation to take market share from rivals) and IP policy (ensuring that R&D actually needs to be performed in order to gain a lead) are in a sense complements! The fundamental theoretical driver is that the incentive to innovate depends not only on the rents of an innovation, but on the incremental rents of an innovation; if innovators include firms that already active in an industry, policy that makes your current technological state less valuable (because you are in a more competitive market, say) or policy that makes jumping to a better technological state more valuable both increase the size of the incremental rent, and hence the incentive to perform R&D.

Here are the key aspects of a simplified version of the model. An industry is a duopoly where consumers spend exactly 1 dollar per period. The duopolists produce partially substitutable goods, where the more similar the goods the more “product market competition” there is. Each of the duopolists produces their good at a firm-specific cost, and competes in Bertrand with their duopoly rival. At the minimal amount of product market competition, each firm earns constant profit regardless of their cost or their rival’s cost. Firms can invest in R&D which gives some flow probability of lowering their unit cost. Technological laggards sometimes catch up to the unit cost of leaders with exogenous probability; lower IP protection (or more prevalent spillovers) means this probability is higher. We’ll look only at features of this model in the stochastic distribution of technological leadership and lags which is a steady state if there infinite duopolistic industries.

In a model with these features, you always want at least a little competition, essentially for Arrow (1962) reasons: the size of the market is small when market power is large because total unit sales are low, hence the benefit of reducing unit costs is low, hence no one will bother to do any innovation in the limit. More competition can also be good because it increases the probability that two firms are at similar technological levels, in which case each wants to double down on research intensity to gain a lead. At very high levels of competition, the old Schumpeterian story might bind again: goods are so substitutable that R&D to increase rents is pointless since almost all rents are competed away, especially if IP is weak so that rival firms catch up to your unit cost quickly no matter how much R&D you do. What of the optimal level of IP? It’s always best to ensure IP is not too strong, or that spillovers are not too weak, because the benefit of increased R&D effort when firms are at similar technological levels following the spillover exceeds the lost incentive to gain a lead in the first place when IP is not perfectly strong. When markets are really competitive, however, the Schumpeterian insight that some rents need to exist militates in favor of somewhat stronger IP than in less competitive product markets.

Final working paper (RePEc IDEAS) which was published in 2001 in the Review of Economic Studies. This paper is the more detailed one theoretically, but if all of the insight sounds familiar, you may already know the hugely influential follow-up paper by Aghion, Bloom, Blundell, Griffith and Howitt, “Competition and Innovation: An Inverted U Relationship”, published in the QJE in 2005. That paper gives some empirical evidence for the idea that innovation is maximized at intermediate values of product market competition; the Schumpeterian “we need some rents” motive and the “firms innovate to escape competition” motive both play a role. I am actually not a huge fan of this paper – as an empirical matter, I’m unconvinced that most cost-reducing innovation in many industries will never show up in patent statistics (principally for reasons that Eric von Hippel made clear in The Sources of Innovation, which is freely downloadable at that link!). But this is a discussion for another day! One more related paper we have previously discussed is Goettler and Gordon’s 2012 structural work on processor chip innovation at AMD and Intel, which has a very similar within-industry motivation.

Venture capital financing of innovative firms feels like a new phenomenon, and is clearly of great importance to high tech companies as well as cities that hope to attract these companies. The basic principle involves relatively small numbers of wealthy individuals providing long-term financing to a group of managers who seek out early-stage, unprofitable firms, make an investment (generally equity), and occasionally help actively manage the company.

There are many other ways firms can fund themselves: issuance of equity, investment from friends or family, investment from an existing firm in a spinoff, investment from the saved funds of an individual, or debt loans from a bank, among others. Two questions, then, are immediate: why does anyone fund with VC in the first place, and how did this institutional form come about? VC is strange at first glance: in a stage in which entrepreneur effort is particularly important, why would I write a financing contract which takes away some of the upside of working hard on the part of the entrepreneur by diluting her ownership share? Two things are worth noting. VC rather than debt finance is particularly common when returns are highly skewed – a bank loan can only be repaid with interest, hence will have trouble capturing that upside. Second, early-stage equity finance and active managerial assistance appear to come bundled, hence some finance folks have argued that the moral hazard problem lies both with the entrepreneur, who must be incentivized to work hard, and with the VC firm and their employees, who need the same incentive.

Let’s set aside the question of entrepreneurial finance, and look into history. Though something like venture capital appeared to be important in the Second Industrial Revolution (see, e.g., Lamoreaux et al (2006) on that hub of high-tech, Cleveland!), and it may have existed in a proto-form as early as the 1700s with the English country banks (though I am not totally convinced of that equivalence), the earliest modern VC firm was Boston’s American Research and Development Corporation. The decline of textiles hit New England hard in the 1920s and 1930s. A group of prominent blue bloods, including the President of MIT and the future founder of INSEAD, had discussed the social need for an organization that would fund firms which could potentially lead to new industries, and they believed that despite this social goal, the organization ought be a profit-making concern if it were to be successful in the long run.

After a few false starts, the ARD formed in 1946, a time of widespread belief in the power of R&D following World War II and Vannevar Bush’s famous “Science: the Endless Frontier”. ARD was organized as a closed-end investment trust, which permitted institutional investors to contribute. Investments tended to be solicited, were very likely to be made to New England firms, and were, especially in the first few years, concentrated in R&D intensive companies; local, solicited, R&D heavy investment is even today the most common type of VC. Management was often active, and there are reports of entire management teams being replaced by ARD if they felt the firm was not growing quickly enough.

So why have you never of ARD, then? Two reasons: returns, and organizational structure. ARD’s returns over the 50s and 60s were barely higher, even before fees, than the S&P 500 as a whole. And this overstates things: an investment in Digital Equipment, the pioneering minicomputer company, was responsible for the vast majority of profits. No surprise, then, that even early VCs had highly skewed returns. More problematic was competition. A 1958 law permitted Small Business Investment Corporations (SBICs) to make VC-style investments at favorable tax rates, and the organizational form of limited partnership VC was less constrained by the SEC than a closed-end investment fund. In particular, the partnerships “2 and 20″ structure meant that top investment managers could earn much more money at that type of firm than at ARD. One investment manager at ARD put a huge amount of effort into developing a company called Optical Scanning, whose IPO made the founder $10 million. The ARD employee, partially because of SEC regulations, earned a $2000 bonus. By 1973, ARD had been absorbed into another company, and was for all practical purposes defunct.

It’s particularly interesting, though, that the Boston Brahmins were right: VC has been critical in two straight resurgences in the New England economy, the minicomputer cluster of the 1960s, and the more recent Route 128 biotech cluster, both of which were the world’s largest. New England, despite the collapse of textiles, has not gone the way of the rust belt – were it a country, it would be wealthier per capita than all but a couple of microstates. And yet, ARD as a profitmaking enterprise went kaput rather quickly. Yet more evidence of the danger of being a market leader – not only can other firms avoid your mistakes, but they can also take advantage of more advantageous organizational forms and laws that are permitted or created in response to your early success!

Disruption. You can’t read a book about the tech industry without Clayton Christensen’s Innovator’s Dilemma coming up. Jobs loved it. Bezos loved it. Economists – well, they were a bit more confused. Here’s the story at its most elemental: in many industries, radical technologies are introduced. They perform very poorly initially, and so are ignored by the incumbent. These technologies rapidly improve, however, and the previously ignored entrants go on to dominate the industry. The lesson many tech industry folks take from this is that you ought to “disrupt yourself”. If there is a technology that can harm your most profitable business, then you should be the one to develop it; take Amazon’s “Lab126″ Kindle skunkworks as an example.

There are a couple problems with this strategy, however (well, many problems actually, but I’ll save the rest for Jill Lepore’s harsh but lucid takedown of the disruption concept which recently made waves in the New Yorker). First, it simply isn’t true that all innovative industries are swept by “gales of creative destruction” – consider automobiles or pharma or oil, where the major players are essentially all quite old. Gans, Hsu and Scott Stern pointed out in a RAND article many years ago that if the market for ideas worked well, you would expect entrants with good ideas to just sell to incumbents, since the total surplus would be higher (less duplication of sales assets and the like) and since rents captured by the incumbent would be higher (less product market competition). That is, there’s no particular reason that highly innovative industries require constant churn of industry leaders.

The second problem concerns disrupting oneself or waiting to see which technologies will last. Imagine it is costly to investigate potentially disruptive technologies for the incumbent. For instance, selling mp3s in 2002 would have cannibalized existing CD sales at a retailer with a large existing CD business. Early on, the potentially disruptive technology isn’t “that good”, hence it is not in and of itself that profitable. Eventually, some of these potentially disruptive technologies will reveal themselves to actually be great improvements on the status quo. If that is the case, then, why not just let the entrant make these improvements/drive down costs/learn about market demand, and then buy them once they reveal that the potentially disruptive product is actually great? Presumably the incumbent even by this time still retains its initial advantage in logistics, sales, brand, etc. By waiting and buying instead of disrupting yourself, you can still earn those high profits on the CD business in 2002 even if mp3s had turned out to be a flash in the pan.

This is roughly the intuition in a new paper by Matt Marx – you may know his work on non-compete agreements – Gans and Hsu. Matt has also collected a great dataset from industry journals on every firm that ever operated in automated speech recognition. Using this data, the authors show that a policy by entrants of initial competition followed by licensing or acquisition is particularly common when the entrants come in with a “disruptive technology”. You should see these strategies, where the entrant proves the value of their technology and the incumbent waits to acquire, in industries where ideas are not terribly appropriable (why buy if you can steal?) and entry is not terribly expensive (in an area like biotech, clinical trials and the like are too expensive for very small firms). I would add that you also need complementary assets to be relatively hard to replicate; if they aren’t, the incumbent may well wind up being acquired rather than the entrant should the new technology prove successful!

This paper, by Heidi Williams (who surely you know already) and Bhaven Sampat (who is perhaps best known for his almost-sociological work on the Bayh-Dole Act with Mowery), made quite a stir at the NBER last week. Heidi’s job market paper a few years ago, on the effect of openness in the Human Genome Project as compared to Celera, is often cited as an “anti-patent” paper. Essentially, she found that portions of the human genome sequenced by the HGP, which placed their sequences in the public domain, were much more likely to be studied by scientists and used in tests than portions sequenced by Celera, who initially required fairly burdensome contractual steps to be followed. This result was very much in line with research done by Fiona Murray, Jeff Furman, Scott Stern and others which also found that minor differences in openness or accessibility can have substantial impacts on follow-on use (I have a paper with Yasin Ozcan showing a similar result). Since the cumulative nature of research is thought to be critical, and since patents are a common method of “restricting openness”, you might imagine that Heidi and the rest of these economists were arguing that patents were harmful for innovation.

That may in fact be the case, but note something strange: essentially none of the earlier papers on open science are specifically about patents; rather, they are about openness. Indeed, on the theory side, Suzanne Scotchmer has a pair of very well-known papers arguing that patents effectively incentivize cumulative innovation if there are no transaction costs to licensing, no spillovers from sequential research, and no incentive for early researchers to limit licenses in order to protect their existing business (consider the case of Farnsworth and the FM radio), and if potential follow-on innovators can be identified before they sink costs. That is a lot of conditions, but it’s not hard to imagine industries where inventions are clearly demarcated, where holders of basic patents are better off licensing than sitting on the patent (perhaps because potential licensors are not also competitors), and where patentholders are better off not bothering academics who technically infringe on their patent.

What industry might have such characteristics? Sampat and Williams look at gene patents. Incredibly, about 30 percent of human genes have sequences that are claimed under a patent in the United States. Are “patented genes” still used by scientists and developers of medical diagnostics after the patent grant, or is the patent enough of a burden to openness to restrict such use? What is interesting about this case is that the patentholder generally wants people to build on their patent. If academics find some interesting genotype-phenotype links based on their sequence, or if another firm develops a disease test based on the sequence, there are more rents for the patentholder to garner. In surveys, it seems that most academics simply ignore patents of this type, and most gene patentholders don’t interfere in research. Anecdotally, licenses between the sequence patentholder and follow-on innovators are frequent.

In general, it is really hard to know whether patents have any effect on anything, however; there is very little variation over time and space in patent strength. Sampat and Williams take advantage of two quasi-experiments, however. First, they compare applied-for-but-rejected gene patents to applied-for-but-granted patents. At least for gene patents, there is very little difference in terms of measurables before the patent office decision across the two classes. Clearly this is not true for patents as a whole – rejected patents are almost surely of worse quality – but gene patents tend to come from scientifically competent firms rather than backyard hobbyists, and tend to have fairly straightforward claims. Why are any rejected, then? The authors’ second trick is to look directly at patent examiner “leniency”. It turns out that some examiners have rejection rates much higher than others, despite roughly random assignment of patents within a technology class. Much of the difference in rejection probability is driven by the random assignment of examiners, which justifies the first rejected-vs-granted technique, and also suggested an instrumental variable to further investigate the data.

With either technique, patent status essentially generates no difference in the use of genes by scientific researchers and diagnostic test developers. Don’t interpret this result as turning over Heidi’s earlier genome paper, though! There is now a ton of evidence that minor impediments to openness are harmful to cumulative innovation. What Sampat and Williams tell us is that we need to be careful in how we think about “openness”. Patents can be open if the patentholder has no incentive to restrict further use, if downstream innovators are easy to locate, and if there is no uncertainty about the validity or scope of a patent. Indeed, in these cases the patentholder will want to make it as easy as possible for follow-on innovators to build on their patent. On the other hand, patentholders are legally allowed to put all sorts of anti-openness burdens on the use of their patented invention by anyone, including purely academic researchers. In many industries, such restrictions are in the interest of the patentholder, and hence patents serve to limit openness; this is especially true where private sector product development generates spillovers. Theory as in Scotchmer-Green has proven quite correct in this regard.

One final comment: all of these types of quasi-experimental methods are always a bit weak when it comes to the extensive margin. It may very well be that individual patents do not restrict follow-on work on that patent when licenses can be granted, but at the same time the IP system as a whole can limit work in an entire technological area. Think of something like sampling in music. Because all music labels have large teams of lawyers who want every sample to be “cleared”, hip-hop musicians stopped using sampled beats to the extent they did in the 1980s. If you investigated whether a particular sample was less likely to be used conditional on its copyright status, you very well might find no effect, as the legal burden of chatting with the lawyers and figuring out who owns what may be enough of a limit to openness that musicians give up samples altogether. Likewise, in the complete absence of gene patents, you might imagine that firms would change their behavior toward research based on sequenced genes since the entire area is more open; this is true even if the particular gene sequence they want to investigate was unpatented in the first place, since having to spend time investigating the legal status of a sequence is a burden in and of itself.

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It’s been a while – a month of exploration in the hinterlands of the former Soviet Union, a move up to Canada, and a visit down to the NBER Summer Institute really put a cramp on my posting schedule. That said, I have a ridiculously long backlog of posts to get up, so they will be coming rapidly over the next few weeks. I saw today’s paper presented a couple days ago at the Summer Institute. (An aside: it’s a bit strange that there isn’t really any media at SI – the paper selection process results in a much better set of presentations than at the AEA or the Econometric Society, which simply have too long of a lag from the application date to the conference, and too many half-baked papers.)

Bustos and her coauthors ask, when can improvements in agricultural productivity help industrialization? An old literature assumed that any such improvement would help: the newly rich agricultural workers would demand more manufactured goods, and since manufactured and agricultural products are complements, rising agricultural productivity would shift workers into the factories. Kiminori Matsuyama wrote a model (JET 1992) showing the problem here: roughly, if in a small open economy productivity goes up in a good you have a Ricardian comparative advantage in, then you want to produce even more of that good. A green revolution which doubles agricultural productivity in, say, Mali, while keeping manufacturing productivity the same, will allow Mali to earn twice as much selling the agriculture overseas. Workers will then pour into the agricultural sector until the marginal product of labor is re-equated in both sectors.

Now, if you think that industrialization has a bunch of positive macrodevelopment spillovers (via endogenous growth, population control or whatever), then this is worrying. Indeed, it vaguely suggests that making villages more productive, an outright goal of a lot of RCT-style microdevelopment studies, may actually be counterproductive for the country as a whole! That said, there seems to be something strange going on empirically, because we do appear to see industrialization in countries after a Green Revolution. What could be going on? Let’s look back at the theory.

Implicitly, the increase in agricultural productivity in Matsuyama was “Hicks-neutral” – it increased the total productivity of the sector without affecting the relative marginal factor productivities. A lot of technological change, however, is factor-biased; to take two examples from Brazil, modern techniques that allow for double harvesting of corn each year increase the marginal productivity of land, whereas “Roundup Ready” GE soy that requires less tilling and weeding increases the marginal productivity of farmers. We saw above that Hicks-neutral technological change in agriculture increases labor in the farm sector: workers choosing where to work means that the world price of agriculture times the marginal product of labor in that sector must be equal to world price of manufacturing times the marginal product of labor in manufacturing. A Hicks-neutral improvement in agricultural productivity raises MPL in that sector no matter how much land or labor is currently being used, hence wage equality across sectors requires workers to leave the factor for the farm.

What of biased technological change? As before, the only thing we need to know is whether the technological change increases the marginal product of labor. Land-augmenting technical change, like double harvesting of corn, means a country can produce the same amount of output with the old amount of farm labor and less land. If one more worker shifts from the factory to the farm, she will be farming less marginal land than before the technological change, hence her marginal productivity of labor is higher than before the change, hence she will leave the factory. Land-augmenting technological change always increases the amount of agricultural labor. What about farm-labor-augmenting technological change like GM soy? If land and labor are not very complementary (imagine, in the limit, that they are perfect substitutes in production), then trivially the marginal product of labor increases following the technological change, and hence the number of farm workers goes up. The situation is quite different if land and farm labor are strong complements. Where previously we had 1 effective worker per unit of land, following the labor-augmenting technology change it is as if we have, say, 2 effective workers per unit of land. Strong complementarity implies that, at that point, adding even more labor to the farms is pointless: the marginal productivity of labor is decreasing in the technological level of farm labor. Therefore, labor-augmenting technology with a strongly complementary agriculture production function shifts labor off the farm and into manufacturing.

That’s just a small bit of theory, but it really clears things up. And even better, the authors find empirical support for this idea: following the introduction to Brazil of labor-augmenting GM soy and land-augmenting double harvesting of maize, agricultural productivity rose everywhere, the agricultural employment share rose in areas that were particularly suitable for modern maize production, and the manufacturing employment share rose in areas that were particularly suitable for modern soy production.

August 2013 working paper. I think of this paper as a nice complement to the theory and empirics in Acemoglu’s Directed Technical Change and Walker Hanlon’s Civil War cotton paper. Those papers ask how changes in factor prices endogenously affect the development of different types of technology, whereas Bustos and coauthors ask how the exogenous development of different types of technology affect the use of various factors. I read the former as most applicable to structural change questions in countries at the technological frontier, and the latter as appropriate for similar questions in developing countries.

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Research Policy, the premier journal for innovation economists, recently produced a symposium on the work of Nick von Tunzelmann. Tunzelmann is best known for exploring the social value of the invention of steam power. Many historians had previously granted great importance to the steam engine as a driver of the Industrial Revolution. However, as with Fogel’s argument that the railroad was less important to the American economy than previously believed (though see Donaldson and Hornbeck’s amendment claiming that market access changes due to rail were very important), the role of steam in the Industrial Revolution may have been overstated.

This is surprising. To my mind, the four most important facts for economics to explain is why the world economy (in per capita terms) stagnated until the early 1800s, why cumulative per-capita growth began then in a corner of Northwest Europe, why growth at the frontier has continued to the present, and why growth at the frontier has been so consistent over this period. The consistency is really surprising, given that individual non-frontier country growth rates, and World GDP growth, has vacillated pretty wildly on a decade-by-decade basis.

Malthus’ explanation still explains the first puzzle best. But there remain many competing explanations for how exactly the Malthusian trap was broken. The idea that a thrifty culture or expropriation of colonies was critical sees little support from economic historians; as McCloskey writes, “Thrifty self-discipline and violent expropriation have been too common in human history to explain a revolution utterly unprecedented in scale and unique to Europe around 1800.” The problem, more generally, of explaining a large economic X on the basis of some invention/program/institution Y is that basically everything in the economic world is a complement. Human capital absent good institutions has little value, modern management techniques absent large markets is ineffective, etc. The problem is tougher when it comes to inventions. Most “inventions” that you know of have very little immediate commercial importance, and a fair ex-post reckoning of the critical parts of the eventual commercial product often leaves little role for the famous inventor.

What Tunzelmann and later writers in his tradition point out is that even though Watt’s improvement to the steam engine was patented in 1769, steam produces less horsepower than water in the UK as late as 1830, and in the US as late as the Civil War. Indeed, even today, hydropower based on the age-old idea of the turbine is still an enormous factor in the siting of electricity-hungry industries. It wasn’t until the invention of high-pressure steam engines like the Lancanshire boiler in the 1840s that textile mills really saw steam power as an economically viable source of energy. Most of the important inventions in the textile industry were designed originally for non-steam power sources.

The economic historian Nicholas Crafts supports Tunzelmann’s original argument against the importance of steam using a modern growth accounting framework. Although the cost of steam power fell rapidly following Watt, and especially after the Corliss engine in the mid 19th century, steam was still a relatively small part of economy until the mid-late 19th century. Therefore, even though productivity growth within steam was quick, only a tiny portion of overall TFP growth in the early Industrial Revolution can be explained by steam. Growth accounting exercises have a nice benefit over partial equilibrium social savings calculations because the problem that “everything is a complement” is taken care of so long as you believe the Cobb-Douglas formulation.

R&D decisions are not made in a vacuum: my firm both benefits from information about new technologies discovered by others, and is harmed when other firms create new products that steal from my firm’s existing product lines. Almost every workhorse model in innovation is concerned with these effects, but measuring them empirically, and understanding how they interact, is difficult. Bloom, Schankerman and van Reenen have a new paper with a simple but clever idea for understanding these two effects (and it will be no surprise to readers given how often I discuss their work that I think these three are doing some of the world’s best applied micro work these days).

First, note that firms may be in the same technology area but not in the same product area; Intel and Motorola work on similar technologies, but compete on very few products. In a simple model, firms first choose R&D, knowledge is produced, and then firms compete on the product market. The qualitative results of this model are as you might expect: firms in a technology space with many other firms will be more productive due to spillovers, and may or may not actually perform more R&D depending on the nature of diminishing returns in the knowledge production function. Product market rivalry is always bad for profits, does not affect productivity, and increases R&D only if research across firms is a strategic complement; this strategic complementarity could be something like a patent race model, where if firms I compete with are working hard trying to invent the Next Big Thing, then I am incentivized to do even more R&D so I can invent first.

On the empirical side, we need a measure of “product market similarity” and “technological similarity”. Let there be M product classes and N patent classes, and construct vectors for each firm of their share of sales across product classes and share of R&D across patent classes. There are many measures of the similarity of a vector, of course, including a well-known measure in innovation from Jaffe. Bloom et al, after my heart, note that we really ought use measures that have proper axiomatic microfoundations; though they do show the properties of a variety of measures of similarity, they don’t actually show the existence (or impossibility) of their optimal measure of similarity. This sounds like a quick job for a good microtheorist.

With similarity measures, all that’s left to do is run regressions of technological and product market similarity, as well as all sorts of fixed effects, on outcomes like R&D performed, productivity (measured using patents or out of a Cobb-Douglas equation) and market value (via the Griliches-style Tobin’s Q). These guys know their econometrics, so I’m omitting many details here, but I should mention that they do use the idea from Wilson’s 2009 ReSTAT of basically random changes in state R&D tax laws as an IV for the cost of R&D; this is a great technique, and very well implemented by Wilson, but getting these state-level R&D costs is really challenging and I can easily imagine a future where the idea is abused by naive implementation.

The results are actually pretty interesting. Qualitatively, the empirical results look quite like the theory, and in particular, the impact of technological similarity looks really important; having lots of firms working on similar technologies but working in different industries is really good for your firm’s productivity and profits. Looking at a handful of high-tech sectors, Bloom et al estimate that the marginal social return on R&D is on the order of 40 percentage points higher than the marginal private return of R&D, implying (with some huge caveats) that R&D in the United States might be something like 3 times smaller than it ought to be. This estimate is actually quite similar to what researchers using other methods have estimated. Interestingly, since bigger firms tend to work in more dense parts of the technology space, they tend to generate more spillovers, hence the common policy prescription of giving smaller firms higher R&D tax credits may be a mistake.

Two caveats. As far as I can tell, the model does not allow a role for absorptive capacity, where firm’s ability to integrate outside knowledge is endogenous to their existing R&D stock. Second, the estimated marginal private rate of return on R&D is something like 20 percent for the average firm; many other papers have estimated very high private benefits from research, but I have a hard time interpreting these estimates. If there really are 20% rates of return lying around, why aren’t firms cranking up their research? At least anecdotally, you hear complaints from industries like pharma about low returns from R&D. Third, there are some suggestive comments near the end about how government subsidies might be used to increase R&D given these huge social returns. I would be really cautious here, since there is quite a bit of evidence that government-sponsored R&D generates a much lower private and social rate of return that the other forms of R&D.

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Simultaneous discovery, as famously discussed by Merton, is really a fascinating idea. On the one hand, we have famous examples like Bell and Gray sending in patents for a telephone on exactly the same day. On the other hand, when you investigate supposed examples of simultaneous discovery more closely, it is rarely the case that the discoveries are that similar. The legendary Jacob Schmookler described – in a less-than-politically-correct way! – historians who see patterns of simultaneous discovery everywhere as similar to tourists who think “all Chinamen look alike.” There is sufficient sociological evidence today that Schmookler largely seems correct: simultaneous discovery, like “lucky” inventions, are much less common than the man on the street believes (see, e.g., Simon Schaeffer’s article on the famous story of the dragon dream and the invention of benzene for a typical reconstruction of how “lucky” inventions actually happen).

Michaël Bikard thinks we are giving simultaneous discovery too little credit as a tool for investigating important topics in the economics of innovation. Even if simultaneous discovery is uncommon, it still exists. If there were an automated process to generate a large set of simultaneous inventions (on relatively more minor topics than the telephone), there are tons of interesting questions we can answer, since we would have compelling evidence of the same piece of knowledge existing in different places at the same time. For instance, how important are agglomeration economies? Does a biotech invention get developed further if it is invented on Route 128 in Massachusetts instead of in Lithuania?

Bikard has developed an automated process to do this (and that linked paper also provides a nice literature review concerning simultaneous discovery). Just scrape huge number of articles and their citations, look for pairs of papers which were published at almost the same time and cited frequently in the future, and then limit further to articles which have a “Jaccard index” which implies that they are frequently cited together if they are cited at all. Applying this technique to the life sciences, he finds 578 examples of simultaneous discovery; chatting with a randomly selected sample of the researchers, most mentioned the simultaneous discovery without being asked, though at least one claimed his idea had been stolen! 578 is a ton: this is more than double the number that the historical analysis in Merton discovered, and as noted, many of the Merton multiples are not really examples of simultaneous discovery at all.

He then applies this dataset in a second paper, asking whether inventions in academia are used more often (because of the culture of openness) or whether private sector inventions are used more often in follow-up inventions (because the control rights can help even follow-up inventors extract rents). It turns out that private-sector inventors of the identical invention are three times more likely to patent, but even excluding the inventors themselves, the private sector inventions are cited 10-20% more frequently in future patents. The sample size of simultaneous academic-private discovery is small, so this evidence is only suggestive. You might imagine that the private sector inventors are more likely to be colocated near other private sector firms in the same area; we think that noncodified aspects of knowledge flow locally, so it wouldn’t be surprising that the private sector multiple was cited more often in future patents.

Heavy caveats are also needed on the sample. This result certainly doesn’t suggest that, overall, private sector workers are doing more “useful” work than Ivory Tower researchers, since restricting the sample to multiple discoveries limits the potential observations to areas where academia and the private sector are working on the same type of discovery. Certainly, academics and the private sector often work on different types of research, and openness is probably more important in more basic discoveries (where transaction or bargaining costs on follow-up uses are more distortionary). In any case, the method for identifying simultaneous discoveries is quite interesting indeed; if you are empirically minded, there are tons of interesting questions you could investigate with such a dataset.